
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "124"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 102 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 98 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	39.786488	11.678570	59.052290	75.488663	19.256125	26.890160	31.599959	30.910996	0.0420	0.0494	0.0072	1.253276	1.235358
2459593	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	37.334897	15.324243	71.225663	80.203089	29.872019	36.458459	22.129831	11.657965	0.0412	0.0484	0.0089	1.221526	1.238026
2459592	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	2.698629	1.624756	2.159486	4.052849	1.992545	0.825621	2.842334	0.478674	0.0283	0.0273	0.0014	nan	nan
2459591	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	33.054530	18.636747	84.217570	92.879255	26.600317	32.185469	11.709057	2.037818	0.0418	0.0505	0.0087	1.280015	1.300398
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	40.957798	22.017692	78.126624	86.611245	28.000595	33.714232	17.261057	4.979851	0.0449	0.0530	0.0079	1.300030	1.282817
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	34.734479	18.245411	72.814740	80.290051	16.602295	21.498794	22.045401	16.737728	0.0432	0.0516	0.0079	1.220122	1.202224
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	49.078366	14.915195	84.480600	97.889128	21.805955	24.269683	21.862152	13.372788	0.0423	0.0492	0.0085	1.281936	1.312164
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	31.912239	19.479177	118.440392	130.942889	5.792803	8.220349	14.534285	15.645600	nan	nan	nan	1.071825	1.064448
2459586	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	35.207687	16.377745	28.179602	30.381966	9.587745	11.099274	19.857008	6.624049	0.0452	0.0538	0.0079	1.256481	1.287281
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.258123	26.473495	7.076175	8.893701	9.066912	10.124246	6.160352	3.274966	0.0303	0.0295	0.0020	0.000000	0.000000
2459584	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.874383	26.299913	97.193002	105.388795	25.456929	36.583403	39.004076	45.268887	0.0426	0.0479	0.0062	0.000000	0.000000
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.024204	17.519887	30.487173	33.086608	19.002003	22.352743	-0.116519	-0.020985	0.0455	0.0494	0.0066	0.000000	0.000000
2459582	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.213102	15.123854	27.369038	30.016475	22.800795	27.595035	-0.085481	0.080701	0.0430	0.0457	0.0052	1.218840	1.216507
2459581	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.831243	21.038280	31.731991	33.727346	17.941371	21.858289	0.103250	0.103028	0.0465	0.0506	0.0059	1.175638	1.173335
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.596359	15.731096	31.817554	34.577789	19.869749	23.146836	-0.322846	-0.042866	0.0478	0.0516	0.0055	0.954231	0.956791
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.361585	15.603091	36.050400	39.292718	22.317460	26.231916	0.764847	1.067271	0.0434	0.0473	0.0049	1.220817	1.219840
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.775899	1.923216	0.383017	0.717699	-0.101875	-0.467145	-0.307860	0.250491	0.0324	0.0290	0.0011	nan	nan
2459577	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.385566	20.147952	29.460169	31.528437	20.140934	23.861094	-0.046906	0.080164	0.0426	0.0471	0.0058	1.186675	1.213759
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.314937	15.882054	23.414238	25.303308	18.358754	21.220562	0.596641	0.673967	0.0449	0.0489	0.0056	1.225984	1.250878
2459575	digital_ok	100.00%	0.00%	41.11%	0.00%	100.00%	0.00%	0.083479	1.664157	-0.237038	4.558027	-1.317846	80.672177	0.127381	1.015115	0.7817	0.5150	0.3921	7.046563	5.847709
2459574	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.194766	0.770057	-0.178998	-0.396839	0.357527	-0.084385	8.773147	9.218534	0.7228	0.7315	0.3645	5.938241	5.410486
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.746779	1.044007	-0.277440	-0.727293	-1.018429	-1.441349	-0.164503	0.152141	0.7214	0.7303	0.3672	1.703495	1.568003
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.849036	-0.579704	0.658142	0.499369	0.745444	-1.280541	-0.166793	0.315954	0.0290	0.0281	0.0011	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.54%	0.570620	0.862661	-0.113700	-0.572805	-0.332280	-0.675909	-0.020678	-0.570613	0.7939	0.7964	0.2186	2.191160	1.879754
2459566	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.504781	0.989987	-0.130262	-0.513422	-0.496312	-0.565570	-0.237143	-0.382797	0.0614	0.0644	0.0056	1.262671	1.261851
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	4180.067458	4180.067458	3921.574600	3921.574600	-0.652453	-0.652453	-1.812525	-1.812525	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	1.73%	0.135526	-0.608916	-1.515296	-0.213682	1.964207	-0.029243	0.388658	0.037628	0.7337	0.7325	0.3787	1.746247	1.532818
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	1.227900	1.155363	1.190579	-0.360646	-0.622079	-1.013647	-0.270235	-0.313989	0.7256	0.7302	0.3734	1.881285	1.653634
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.263454	-0.699285	0.180124	0.014753	0.710596	0.829589	0.511738	0.281305	0.7300	0.7373	0.3656	1.855139	1.614511
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.166572	-0.593932	-0.001545	-0.171577	0.163601	0.176088	0.085664	-0.250823	0.7281	0.7381	0.3632	1.812434	1.614281
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.143939	-0.431757	0.197667	-0.146280	0.696992	0.614681	0.014974	-0.203510	0.7207	0.7294	0.3821	1.804541	1.560842
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	0.353939	-0.528973	0.112406	-0.222395	0.506593	0.389401	0.032468	-0.280897	0.7254	0.7315	0.3713	1.766770	1.550760
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	1.072786	0.894833	-0.400068	-0.460081	-1.029544	-1.376672	-0.084214	-0.064945	0.7303	0.7333	0.3740	1.819616	1.532964
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0294	0.0285	0.0008	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.247003	0.745789	-0.461863	-0.318723	-0.671853	-0.368161	-0.242557	0.092019	0.7267	0.7316	0.3734	1.770473	1.542057
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7295	0.7356	0.3823	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7271	0.7322	0.3827	nan	nan
2459552	digital_ok	100.00%	18.38%	0.00%	0.00%	100.00%	0.00%	1.358398	2.716153	3.255504	-0.656312	114.039279	5.020536	14.881549	0.653419	0.6202	0.7324	0.4091	10.029099	8.815028
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.119175	-0.597602	0.116691	-0.268997	0.593026	0.400382	0.153364	0.177995	0.7271	0.7329	0.3766	1.932231	1.629427
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0289	0.0285	0.0007	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.249451	1.103853	-0.338947	-0.828270	-0.667560	-0.783421	-0.368762	-0.640966	0.7274	0.7345	0.3830	2.004609	1.629850
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	-0.018938	1.142838	-0.409047	-0.779562	0.486797	-0.724244	-0.240792	-0.316769	0.7778	0.7815	0.3330	2.006482	1.618218
2459541	digital_ok	0.00%	1.34%	1.79%	0.00%	21.88%	0.00%	1.101333	1.032957	-0.594673	-0.976348	-0.550027	-1.195703	-0.980407	-1.269289	0.7714	0.7651	0.3084	1.683962	1.535381
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.45%	-0.040344	-0.124929	-0.048812	-0.227476	-0.656495	-0.237176	0.021499	-0.171539	0.7768	0.7759	0.3201	1.779370	1.490931
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	0.639111	1.998832	0.139934	-0.771490	-0.083445	-0.686317	-0.539266	-1.218768	0.7608	0.7602	0.3879	1.646352	1.443548
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.373383	-0.257862	0.082393	-0.256346	-0.483834	-0.916461	-0.082578	-0.245280	0.8060	0.7967	0.3304	2.117680	1.607262
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	-0.400528	-0.585958	0.128855	-0.247801	-0.241976	-0.110309	-0.018695	-0.302063	0.7819	0.7755	0.3522	1.619938	1.542928
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	-0.370824	0.725061	-0.533469	-0.810264	-0.826480	-1.031487	-0.149366	0.001468	0.7477	0.7526	0.3777	1.744998	1.512022
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.56%	0.564722	1.360917	-0.295983	-0.718718	-1.389725	-1.370813	-0.385141	-0.700560	0.7510	0.7527	0.3743	1.860101	1.533620
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.268464	0.786525	-0.297085	-0.767224	0.009353	-0.107655	-0.406852	-0.343985	0.9177	0.9499	0.2587	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	12.24%	2.04%	0.215381	1.377966	0.167131	-0.857343	-0.431325	-0.562990	0.074491	-0.510151	nan	nan	nan	2.139223	1.748530
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.239518	1.136435	-0.251009	-0.881228	0.587601	1.459112	-0.296532	-0.619810	0.7273	0.7435	0.3167	2.238410	2.175165
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.912064	-0.672870	0.288600	0.085707	-1.203616	0.202980	-0.734711	-0.161440	0.0358	0.0331	0.0019	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	-0.039288	0.720538	-0.468605	-0.934211	-1.229174	-1.232802	-0.999606	-1.197369	0.9315	0.9615	0.1402	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.069151	2.600441	-0.251968	-0.456783	-1.331185	-0.517409	-1.074412	-0.501855	0.8939	0.8795	0.1801	4.401750	5.396029
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	0.835151	0.781811	-0.063337	-0.782732	-0.276080	-0.776743	0.250782	0.614218	0.8952	0.8935	0.3887	0.000000	0.000000
2459501	digital_ok	0.00%	0.00%	0.00%	0.00%	76.74%	0.00%	0.072848	1.428195	-0.832888	-0.562720	-1.374366	-1.312695	-0.675641	-0.242028	0.8404	0.8790	0.3634	0.000000	0.000000
2459500	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.445105	11.926743	7.975946	7.208621	14.179380	10.576257	4.527598	10.283792	0.8065	0.8567	0.4047	0.000000	0.000000
2459499	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	-0.608996	0.660052	-0.430071	-1.159662	-0.530502	-0.881418	-0.320632	-0.461911	0.8778	0.9347	0.1878	0.000000	0.000000
2459498	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.602981	0.442072	-0.201537	-0.754874	0.020973	-0.859670	0.666515	3.037761	0.8796	0.8993	0.3920	0.000000	0.000000
2459497	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.130301	2.345941	-0.488447	-0.050861	-0.334938	-0.705877	-0.063844	5.151282	0.0997	0.1013	0.0158	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.062539	0.447041	0.306677	-0.809896	0.284582	-0.752379	-0.442374	0.167672	0.0922	0.0945	0.0110	0.000000	0.000000
2459495	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.996501	2.993029	5.570785	0.464736	8.761424	0.883461	8.620307	15.149399	0.0711	0.1308	0.0181	0.000000	0.000000
2459494	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.946545	19.848901	-0.035748	10.935025	-0.026695	20.680946	-0.289617	26.321087	0.0968	0.0644	0.0216	0.000000	0.000000
2459492	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.017839	1.665553	2.769400	-1.436125	-0.903152	0.096607	0.501156	0.662545	0.1019	0.1138	0.0116	0.000000	0.000000
2459491	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.176831	1.605755	3.304265	-1.344933	0.794073	0.793321	0.097709	2.211257	0.1227	0.1040	0.0115	0.000000	0.000000
2459490	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.167968	2.605359	3.081721	-1.722386	1.178955	0.116011	1.084106	6.468246	0.0946	0.0750	0.0090	0.000000	0.000000
2459489	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.454365	1.641282	2.957031	-1.388216	-0.235778	1.322159	0.856738	0.990639	0.0924	0.0883	0.0070	0.000000	0.000000
2459488	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.142499	1.288786	2.186948	-1.117410	-0.932798	0.227326	0.250716	2.089594	0.1042	0.0946	0.0096	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 124 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 124, Node 9:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶